The present invention relates to signal detection in the presence of noise characterized by non-Gaussian noise models. More specifically, but without limitation thereto, the present invention relates to a signal processor for detecting targets in coherent radar return signals in the presence of non-Gaussian radar clutter.
Conventional detection algorithms derived from the assumption of Gaussian noise typically suppress heavily tailed non-Gaussian clutter by requiring higher thresholds than are required for Gaussian noise, degrading the ability of these algorithms to detect weak signals. Non-Gaussian noise may be observed if the noise is dominated by a few non-Gaussian sources, or if the noise is produced by a changing number of Gaussian or non-Gaussian sources, even if the expected number of sources is large. A variety of univariate and multivariate probability distributions have been proposed to model various types of non-Gaussian data including D. Middleton's class A and B models, K, Weibull, log-normal and discrete Guassian mixture distributions. The compound random variable, Z=AX where A&gt;0 and X has a normal distribution, has a class A, K, or discrete Gaussian mixture distribution if A has a Poisson, Gamma, or discrete distribution, respectively. Likelihood ratio and locally optimum detection algorithms based on non-Gaussian noise models have been developed for various signal types. Theoretical and empirical studies demonstrate that these algorithms have a significant performance improvement over corresponding detection algorithms derived from the assumption that the noise data have normal distributions. In applications such as radar, A may be correlated.
A continued need exists for a coherent radar detector having an improved capability for detecting weak radar return signals in the presence of non-Gaussian noise clutter.